A Conceptual Architecture of Fuzzy Risk-Based Decision Support Systems (R+DSS)

Abstract

This paper introduces a conceptual architecture for Fuzzy Risk-Based Decision Support Systems (R+DSS). This architecture is designed to provide a comprehensive and efficient approach to decision-making procedures in various domains involving assessing and controlling potential risks. The proposed architecture exhibits versatility in its applicability across multiple fields, such as finance, healthcare, engineering, and environmental management. It incorporates these components flexibly and scalable while also being user-friendly. The framework employs fuzzy logic principles such as membership functions, rule sets, and inference methods to facilitate a thorough evaluation of the risk that accommodates the inherent uncertainties and imprecisions characteristic of real-world risk scenarios. The Fuzzy Inference Engine is a versatile and resilient risk analysis tool capable of accommodating diverse data and systems, enabling effective risk mitigation strategies. The adaptability of this architecture to effectively handle complex, uncertain and dynamic environments makes it a promising tool for decision-makers looking to improve risk assessment and management protocols.